Method and apparatus for photovoltaic power forecast based on numerical weather prediction

ABSTRACT

The present disclosure provides a method and an apparatus for photovoltaic power forecast based on numerical weather prediction. The method includes: determining a historical key weather feature matrix and a prediction key weather feature matrix; determining a historical weather data matrix and a prediction weather data matrix; determining a historical input matrix and a prediction input matrix; combining the historical input matrix and the prediction input matrix; performing singular value decomposition on the combined input matrix to obtain a principal component feature matrix; determining K principal component features corresponding to K historical time periods having the nearest Manhattan distances with the prediction time period; acquiring a fitting relationship according to the K principal component features and K photovoltaic powers corresponding to the K historical time periods; inputting the principal component feature corresponding to the prediction time period to the fitting relationship to obtain a photovoltaic power.

CROSS REFERENCE TO RELATED APPLICATION

This application is based upon and claims a priority to Chinese PatentApplication Serial No. 201710492720.8, filed with the State IntellectualProperty Office of P. R. China on Jun. 26, 2017, the entire contents ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a field of new energy technologies inpower systems, and more particularly, to a method for photovoltaic powerforecast based on numerical weather prediction, an apparatus forphotovoltaic power forecast based on numerical weather prediction and acomputer-readable storage medium.

BACKGROUND

The photovoltaic power station is a typical intermittent power sourcebecause it can only generate electricity by daylight. The photovoltaicpower of the photovoltaic power station has great volatility andrandomness due to the influence of weather and environment on thephotovoltaic power station. Therefore, it may cause adverse effects onpower grids when large-scale photovoltaic power stations are merged intopower grids.

SUMMARY

Embodiments of the present disclosure provide a method for photovoltaicpower forecast based on numerical weather prediction. The methodincludes: reading from a computer storage device, a plurality of firsteffective light intensities corresponding to a plurality of historicaltime periods and a plurality of first photovoltaic array temperaturescorresponding to the plurality of historical time periods; reading fromthe computer storage device, a plurality of second effective lightintensities corresponding to a plurality of prediction time periods anda plurality of second photovoltaic array temperatures corresponding tothe plurality of prediction time periods; determining a historical keyweather feature matrix based on the plurality of first effective lightintensities and the plurality of first photovoltaic array temperatures;determining a prediction key weather feature matrix based on theplurality of second effective light intensities and the plurality ofsecond photovoltaic array temperatures; reading from the computerstorage device, a plurality of first groups of weather datacorresponding to the plurality of historical time periods; determining ahistorical weather data matrix based on the plurality of first groups ofweather data; reading from the computer storage device, a plurality ofsecond groups of weather data corresponding to the plurality ofprediction time periods; determining a prediction weather data matrixbased on the plurality of second groups of weather data; determining ahistorical input matrix based on the historical key weather featurematrix and the historical weather data matrix; determining a predictioninput matrix based on the prediction key weather feature matrix and theprediction weather data matrix; combining the historical input matrixand the prediction input matrix to determine an input matrix; performingsingular value decomposition on the input matrix to obtain a principalcomponent feature matrix; for a principal component featurecorresponding to each prediction time period in the principal componentfeature matrix, calculating a Manhattan distance between the principalcomponent feature corresponding to each prediction time period and aprincipal component feature corresponding to each historical timeperiod; determining K principal component features corresponding to Khistorical time periods having the nearest Manhattan distances with theprincipal component feature corresponding to each prediction timeperiod; acquiring a fitting relationship according to the K principalcomponent features corresponding to the K historical time periods and Kphotovoltaic powers corresponding to the K historical time periods;inputting the principal component feature corresponding to eachprediction time period to the fitting relationship to obtain aphotovoltaic power corresponding to each prediction time period.

Embodiments of the present disclosure provide an apparatus forphotovoltaic power forecast based on numerical weather prediction. Theapparatus includes a computer storage device, a processor and computerprograms stored in the computer storage device and executable by theprocessor. The computer storage device is configured to store aplurality of first effective light intensities corresponding to aplurality of historical time periods and a plurality of firstphotovoltaic array temperatures corresponding to the plurality ofhistorical time periods, a plurality of second effective lightintensities corresponding to a plurality of prediction time periods anda plurality of second photovoltaic array temperatures corresponding tothe plurality of prediction time periods, a plurality of first groups ofweather data corresponding to the plurality of historical time periods,and a plurality of second groups of weather data corresponding to theplurality of prediction time periods. The processor is configured to byreading the computer programs to: read from the computer storage devicethe plurality of first effective light intensities corresponding to theplurality of historical time periods and the plurality of firstphotovoltaic array temperatures corresponding to the plurality ofhistorical time periods; read from the computer storage device theplurality of second effective light intensities corresponding to theplurality of prediction time periods and the plurality of secondphotovoltaic array temperatures corresponding to the plurality ofprediction time periods; determine a historical key weather featurematrix based on the plurality of first effective light intensities andthe plurality of first photovoltaic array temperatures; determine aprediction key weather feature matrix based on the plurality of secondeffective light intensities and the plurality of second photovoltaicarray temperatures; read from the computer storage device the pluralityof first groups of weather data corresponding to the plurality ofhistorical time periods; determine a historical weather data matrixbased on the plurality of first groups of weather data; read from thecomputer storage device the plurality of second groups of weather datacorresponding to the plurality of prediction time periods; determine aprediction weather data matrix based on the plurality of second groupsof weather data; determine a historical input matrix based on thehistorical key weather feature matrix and the historical weather datamatrix; determine a prediction input matrix based on the prediction keyweather feature matrix and the prediction weather data matrix; combinethe historical input matrix and the prediction input matrix to determinean input matrix; perform singular value decomposition on the inputmatrix to obtain a principal component feature matrix; for a principalcomponent feature corresponding to each prediction time period in theprincipal component feature matrix, calculate a Manhattan distancebetween the principal component feature corresponding to each predictiontime period and a principal component feature corresponding to eachhistorical time period; determine K principal component featurescorresponding to K historical time periods having the nearest Manhattandistances with the principal component feature corresponding to eachprediction time period; acquire a fitting relationship according to theK principal component features corresponding to the K historical timeperiods and K photovoltaic powers corresponding to the K historical timeperiods; input the principal component feature corresponding to eachprediction time period to the fitting relationship to obtain aphotovoltaic power corresponding to each prediction time period.

Embodiments of the present disclosure provide a non-transitorycomputer-readable storage medium having stored therein instructions.When the instructions are executed by a processor of a device, causesthe device to perform acts of: reading from a computer storage device, aplurality of first effective light intensities corresponding to aplurality of historical time periods and a plurality of firstphotovoltaic array temperatures corresponding to the plurality ofhistorical time periods; reading from the computer storage device, aplurality of second effective light intensities corresponding to aplurality of prediction time periods and a plurality of secondphotovoltaic array temperatures corresponding to the plurality ofprediction time periods; determining a historical key weather featurematrix based on the plurality of first effective light intensities andthe plurality of first photovoltaic array temperatures; determining aprediction key weather feature matrix based on the plurality of secondeffective light intensities and the plurality of second photovoltaicarray temperatures; reading from the computer storage device, aplurality of first groups of weather data corresponding to the pluralityof historical time periods; determining a historical weather data matrixbased on the plurality of first groups of weather data; reading from thecomputer storage device, a plurality of second groups of weather datacorresponding to the plurality of prediction time periods; determining aprediction weather data matrix based on the plurality of second groupsof weather data; determining a historical input matrix based on thehistorical key weather feature matrix and the historical weather datamatrix; determining a prediction input matrix based on the predictionkey weather feature matrix and the prediction weather data matrix;combining the historical input matrix and the prediction input matrix todetermine an input matrix; performing singular value decomposition onthe input matrix to obtain a principal component feature matrix; for aprincipal component feature corresponding to each prediction time periodin the principal component feature matrix, calculating a Manhattandistance between the principal component feature corresponding to eachprediction time period and a principal component feature correspondingto each historical time period; determining K principal componentfeatures corresponding to K historical time periods having the nearestManhattan distances with the principal component feature correspondingto each prediction time period; acquiring a fitting relationshipaccording to the K principal component features corresponding to the Khistorical time periods and K photovoltaic powers corresponding to the Khistorical time periods; inputting the principal component featurecorresponding to each prediction time period to the fitting relationshipto obtain a photovoltaic power corresponding to each prediction timeperiod.

Additional aspects and advantages of embodiments of the presentdisclosure will be given in part in the following descriptions, becomeapparent in part from the following descriptions, or be learned from thepractice of the embodiments of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of embodiments of thepresent disclosure will become apparent and more readily appreciatedfrom the following descriptions made with reference to the drawings, inwhich:

FIG. 1 is a flow chart of a method for photovoltaic power forecast basedon numerical weather prediction according to an embodiment of thepresent disclosure.

FIG. 2 is a schematic diagram illustrating daily prediction curves of amethod for photovoltaic power forecast based on numerical weatherprediction according to an embodiment of the present disclosure and amethod for photovoltaic power forecast based on numerical weatherprediction according to the related art.

FIG. 3 is a schematic diagram illustrating weekly prediction curves of amethod for photovoltaic power forecast based on numerical weatherprediction according to an embodiment of the present disclosure and amethod for photovoltaic power forecast based on numerical weatherprediction according to the related art.

FIG. 4 is a block diagram illustrated an apparatus for photovoltaicpower forecast based on numerical weather prediction according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings. The same orsimilar elements and elements having same or similar functions aredenoted by like reference numerals throughout the descriptions. Theembodiments described herein with reference to the accompanying drawingsare explanatory and used to generally understand the present disclosure,which is not construed to limit the embodiments of the presentdisclosure.

The embodiments of the present disclosure disclose a method forphotovoltaic power forecast based on numerical weather prediction. Themethod combines a physical model of the photovoltaic power withdata-driven functionality.

In detail, the physical model of the photovoltaic power is denoted by aformula of:

$\begin{matrix}{{P_{mp} = {P_{{mp}\; 0}{\frac{E_{e}}{E_{0}}\left\lbrack {1 + {\gamma\left( {T_{c} - T_{0}} \right)}} \right\rbrack}}},} & (1)\end{matrix}$where, P_(mp) denotes the photovoltaic power; E_(e) denotes an effectivelight intensity; T_(c) denotes the temperature of a photovoltaic array;E₀ denotes a reference light intensity (E₀ is a constant value, forexample, 1000 W/m²); T₀ denotes a reference temperature (T₀ is aconstant value, for example, 25° C.); P_(mp0) denotes a photovoltaicrating power; and γ denotes a temperature coefficient of thephotovoltaic array (γ is a constant value, depending on factors such asmaterial of the photovoltaic array).

It can be known from the physical model of the photovoltaic power thatweather features directly related to the photovoltaic power are theeffective light intensity and the photovoltaic array temperature.

Based on the physical model of the photovoltaic power, a linearexpression of the photovoltaic power with respect to the effective lightintensity and the photovoltaic array temperature is denoted by a formulaof:

$\begin{matrix}\begin{matrix}{P_{mp} = {P_{{mp}\; 0}{\frac{E_{e}}{E_{0}}\left\lbrack {1 + {\gamma\left( {T_{c} - T_{0}} \right)}} \right\rbrack}}} \\{{= {{\left( {1 - {\gamma\; T_{0}}} \right)\frac{P_{{mp}\; 0}}{E_{0}}E_{e}} + {\gamma\frac{P_{{mp}\; 0}}{E_{0}}E_{e}T_{c}}}},}\end{matrix} & (2)\end{matrix}$where, the formula (2) is the linear expression of P_(mp) with respectto E_(e) and E_(e)·T_(c). Therefore, E_(e) and E_(e)·T_(c) are marked askey weather features that affects the photovoltaic power.

The computer can calculate the above effective light intensities and theabove photovoltaic array temperatures and record them into the computerstorage device.

In an embodiment of the present disclosure, calculating the effectivelight intensity includes the following acts (1-1), (1-2), (1-3) and(1-4).

(1-1) A direct sunlight component, a ground reflection component and asky diffuse component are acquired by a light intensity meter, orthrough a weather database.

(1-2) A photovoltaic array light intensity is acquired based on thedirect sunlight component, the ground reflection component and the skydiffuse component by a formula of:E _(POA) =E _(b) +E _(g) +E _(d)  (3),where, E_(POA) denotes the photovoltaic array light intensity; E_(b)denotes the direct sunlight component; E_(g) denotes the groundreflection component; and E_(d) denotes the sky diffuse component.

(1-3) A photovoltaic array cleanliness is measured.

The photovoltaic array cleanliness may be determined by fieldexperiments.

(1-4) The effective light intensity is acquired based on thephotovoltaic array cleanliness and the photovoltaic array lightintensity by a formula of:E _(e) =E _(POA) ·SF  (4),where, SF denotes the photovoltaic array cleanliness and SF∈[0,1]. WhenSF=1, it indicates that the photovoltaic array is completely clean.

In an embodiment of the present disclosure, calculating the photovoltaicarray temperature includes the following acts (2-1), (2-2), (2-3) and(2-4).

(2-1) The direct sunlight component, the ground reflection component andthe sky diffuse component are acquired based on the act (1-1).

(2-2) The photovoltaic array light intensity is acquired based on theact (1-2).

(2-3) A wind speed and an ambient temperature are measured.

(2-4) The photovoltaic array temperature is acquired based on thephotovoltaic array light intensity, the wind speed and the ambienttemperature by a formula of

$\begin{matrix}{T_{c} = {T_{a} + \frac{E_{POA}}{U_{0} + {U_{1} \cdot {WS}}} + {\frac{E_{POA}}{E_{0}}\Delta\; T}}} & (5)\end{matrix}$where, T_(a) denotes the ambient temperature; WS denotes the wind speed;ΔT denotes a temperature difference coefficient of the photovoltaicarray and indicates a temperature difference between the photovoltaicarray and the photovoltaic element, which is related to factors such asmaterial of the photovoltaic array, in which ΔT>0; U₀ denotes a thermalconductivity constant of the photovoltaic array, for example, 25 W/m²K;and U₁ denotes a thermal convection constant of the photovoltaic array,for example, 6.84 W/m³sK.

FIG. 1 is a flow chart of a method for photovoltaic power forecast basedon numerical weather prediction according to an embodiment of thepresent disclosure. As illustrated in FIG. 1, the method includes actsin the following blocks.

At block 101, a plurality of first effective light intensitiescorresponding to a plurality of historical time periods and a pluralityof first photovoltaic array temperatures corresponding to the pluralityof historical time periods are read from a computer storage device.

At block 102, a plurality of second effective light intensitiescorresponding to a plurality of prediction time periods and a pluralityof second photovoltaic array temperatures corresponding to the pluralityof prediction time periods are read from the computer storage device.

In the embodiments of the present disclosure, acts in block 101 andblock 102 are executed in a non-sequential order.

At block 103, a historical key weather feature matrix is determinedbased on the plurality of first effective light intensities and theplurality of first photovoltaic array temperatures.

In an embodiment of the present disclosure, each historical time periodis one sampling unit time. That is, the unit time for acquiringphotovoltaic power data and weather data. The length of each historicaltime period is not limited, generally from 15 minutes to 1 hour. Forexample, this embodiment selects 1 hour.

For any historical time period t∈{1, 2 . . . T^(h)}, T^(h) denotes thenumber of the plurality of historical time periods and depends on a sizeof historical data samples. For example, the number of historical timeperiods in this embodiment is 8760 hours per year. Assuming that thefirst effective light intensity in the historical time period t isdenoted as E_(e) ^(h)[t] and the first photovoltaic array temperature inthe historical time period t is denoted as T_(c) ^(h)[t], the historicalkey weather feature matrix X_(C) ^(h) in the plurality of historicaltime periods is denoted as follows:

$\begin{matrix}{X_{C}^{h} = {\begin{bmatrix}{E_{e}^{h}\lbrack 1\rbrack} & {{E_{e}^{h}\lbrack 1\rbrack} \cdot {T_{c}^{h}\lbrack 1\rbrack}} \\{E_{e}^{h}\lbrack 2\rbrack} & {{E_{e}^{h}\lbrack 2\rbrack} \cdot {T_{c}^{h}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} & {{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} \cdot {T_{c}^{h}\left\lbrack T^{h} \right\rbrack}}\end{bmatrix}.}} & (6)\end{matrix}$

At block 104, a prediction key weather feature matrix is determinedbased on the plurality of second effective light intensities and theplurality of second photovoltaic array temperatures.

In an embodiment of the present disclosure, each prediction time periodis one sampling unit time. The length of each prediction time period isthe same with that of each historical time period.

For any prediction time period t ∈{1, 2 . . . T^(f)}, T^(f) denotes thenumber of the plurality of prediction time periods and depends on a sizeof prediction data samples. For example, the number of prediction timeperiods in this embodiment is 168 hours per week. Assuming that thesecond effective light intensity in the prediction time period t isdenoted as E_(e) ^(f) [t] and the second photovoltaic array temperaturein the prediction time period t is denoted as T_(e) ^(f) [t], theprediction key weather feature matrix X_(C) ^(f) in the plurality ofprediction time periods is denoted as follows:

$\begin{matrix}{X_{C}^{f} = {\begin{bmatrix}{E_{e}^{f}\lbrack 1\rbrack} & {{E_{e}^{f}\lbrack 1\rbrack} \cdot {T_{c}^{f}\lbrack 1\rbrack}} \\{E_{e}^{f}\lbrack 2\rbrack} & {{E_{e}^{f}\lbrack 2\rbrack} \cdot {T_{c}^{f}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} & {{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} \cdot {T_{c}^{f}\left\lbrack T^{f} \right\rbrack}}\end{bmatrix}.}} & (7)\end{matrix}$

In the embodiments of the present disclosure, acts in block 103 andblock 104 are executed in a non-sequential order.

At block 105, a plurality of first groups of weather data correspondingto the plurality of historical time periods are read from the computerstorage device and a historical weather data matrix is determined basedon the plurality of first groups of weather data.

The historical database of numerical weather forecasts can be stored inthe computer storage device. From the historical database, the pluralityof first groups of weather data corresponding to the plurality ofhistorical time periods can be acquired. Each first group of weatherdata may include one or more of an atmospheric pressure, an airsediment, an air humidity, a sky cloud volume, a wind speed, an airtemperature and a surface heat radiation. The air sediment refers to aparticulate matter deposited in the air.

The historical weather data matrix X_(W) ^(h)∈R^(T) ^(h) ^(×N) isdetermined based on the plurality of first groups of weather data. Eachrow of the matrix represents a historical time period. Each column ofthe matrix represents one type of weather data, and the number of thetypes of weather data is N. The value of N depends on the number ofphysical quantities related to the photovoltaic power in the historicaldatabase. For example, in this embodiment, N is 7, which are: theatmospheric pressure, the air sediment, the air humidity, the sky cloudvolume, the wind speed, the air temperature and the surface heatradiation.

At block 106, a plurality of second groups of weather data correspondingto the plurality of prediction time periods are read from the computerstorage device and a prediction weather data matrix is determined basedon the plurality of second groups of weather data.

The prediction database of numerical weather forecasts can be stored inthe computer storage device. From the prediction database, the pluralityof second groups of weather data corresponding to the plurality ofprediction time periods can be acquired. Each second group of weatherdata may include one or more of an atmospheric pressure, an airsediment, an air humidity, a sky cloud volume, a wind speed, an airtemperature and a surface heat radiation.

The prediction weather data matrix X_(W) ^(f)∈R^(T) ^(f) ^(×N) isdetermined based on the plurality of second groups of weather data. Eachrow of the matrix represents a prediction time period. Each column ofthe matrix represents one type of weather data, and the number of thetypes of weather data is N. The value of N depends on the number ofphysical quantities related to the photovoltaic power in the predictiondatabase. For example, in this embodiment, the types of weather data inthe prediction time periods are the same with that in the historicaltime periods.

In the embodiments of the present disclosure, acts in block 105 andblock 106 are executed in a non-sequential order.

At block 107, a historical input matrix is determined based on thehistorical key weather feature matrix and the historical weather datamatrix.

At block 108, a prediction input matrix is determined based on theprediction key weather feature matrix and the prediction weather datamatrix.

In the embodiments of the present disclosure, acts in block 107 andblock 108 are executed in a non-sequential order.

The historical input matrix is denoted by a formula of:X _(I) ^(f)=[X _(C) ^(f) ,X _(W) ^(f)]  (8).

The prediction input matrix is denoted by a formula of:X _(I) ^(f)=[X _(C) ^(f) ,X _(W) ^(f)]  (8).

At block 109, the historical input matrix is combined with theprediction input matrix to obtain an input matrix.

The input matrix is denoted by

$\begin{bmatrix}X_{I}^{h} \\X_{I}^{f}\end{bmatrix}.$

At block 110, singular value decomposition is performed on the inputmatrix to obtain a principal component feature matrix.

The principal component analysis is applied to the combined input matrixto extract features of the historical input matrix and the predictioninput matrix.

The principal component analysis converts a group of variables that mayhave correlation with each other into a group of linearly uncorrelatedvariables, thereby reducing redundancy of the input matrix andextracting the principal component features.

The singular value decomposition is performed on the input matrix

$\quad\begin{bmatrix}X_{I}^{h} \\X_{I}^{f}\end{bmatrix}$to obtain a principal component feature matrix

$\begin{bmatrix}X_{P}^{h} \\X_{P}^{f}\end{bmatrix},$where, X_(P) ^(h)∈R^(T) ^(h) ^(×L) denotes a principal component featurematrix of the historical time periods, X_(P) ^(f)∈R^(T) ^(f) ^(×L)denotes a principal component feature matrix of the prediction timeperiods, and L denotes the number of the principal component features.

At block 111, for a principal component feature corresponding to eachprediction time period in the principal component feature matrix, aManhattan distance between the principal component feature correspondingto each prediction time period and a principal component featurecorresponding to each historical time period is calculated, and Kprincipal component features corresponding to K historical time periodshaving the nearest Manhattan distances with the principal componentfeature corresponding to each prediction time period are determined.

In detail, the K historical time periods closest to the prediction timeperiod are determined based on a K-nearest neighbor clustering method.That is, for any prediction time period t∈{1, 2 . . . T^(f)}, theManhattan distance between the principal component feature correspondingto this prediction time period and the principal component featurecorresponding to each historical time period is calculated, and the Khistorical time periods having the nearest Manhattan distances areselected. The value of K may affect prediction effect. The best value ofK can be determined by parameter sensitivity analysis duringcross-checking. For example, K=300 is used in this embodiment.

At block 112, a fitting relationship is acquired according to the Kprincipal component features corresponding to the K historical timeperiods and K photovoltaic powers corresponding to the K historical timeperiods.

Based on support vector machine, the relationship between the principalcomponent features of the K historical time periods closest to theprediction time period and the photovoltaic powers corresponding to theK historical time periods closest to the prediction time period may befitted. The fitting relationship of the prediction time period t isobtained as: g_(t): R^(1×L)→R^(1×1), where, g_(t) represents a mappingfunction.

At block 113, the principal component feature corresponding to eachprediction time period is inputted to the fitting relationship to obtaina photovoltaic power corresponding to each prediction time period.

In detail, the principal component feature corresponding to theprediction time period t is inputted to the fitting relationship g_(t)to obtain the photovoltaic power corresponding to the prediction timeperiod t.

In the related art, the method for predicting the photovoltaic power ofthe photovoltaic power station mainly includes the followings.

(1) Collect weather data of the historical time periods, such as theatmospheric pressure, the air sediment, the air humidity, the sky cloudvolume, the wind speed, the air temperature and the surface heatradiation.

(2) Use the weather data of the historical time periods as inputdirectly. Through data-driven algorithms, such as neural network orsupport vector machine, the relationship between historical weather dataand historical photovoltaic power is statistically learned.

(3) Collect weather data of the prediction time periods. Input thisweather data directly to the relationship statistically learned betweenhistorical weather data and historical photovoltaic power, so as toobtain the photovoltaic powers of the prediction time periods.

However, the method in the related art strongly rely on statisticallearning abilities of the data-driven algorithms such as neural networksand support vector machines and ignore the role of the physical model ofthe photovoltaic power on the data-driven algorithms. The physical modelof the photovoltaic power represents an analytical formula among thephotovoltaic power and weather features such as ambient temperature,wind speed and surface light intensity, and implies the physical lawamong the photovoltaic power and weather features.

The calculation results of the method for predicting the photovoltaicpower of the photovoltaic power station according to the embodiments ofthe present disclosure are compared with that of the method forpredicting the photovoltaic power of the photovoltaic power stationaccording to the related art, which are illustrated in FIGS. 2 and 3.All the data comes from the open source data of the 2014 Global LoadForecasting Competition (GEFCom 2014). The data-driven method adopted bythe method in the related art is consistent with that adopted by themethod in the present disclosure, i.e., principal component analysis,the K-nearest neighbor clustering and the support vector machine. FIG. 2is a schematic diagram illustrating daily prediction curves of a methodfor photovoltaic power forecast based on numerical weather predictionaccording to an embodiment of the present disclosure and a method forphotovoltaic power forecast based on numerical weather predictionaccording to the related art. FIG. 3 is a schematic diagram illustratingweekly prediction curves of a method for photovoltaic power forecastbased on numerical weather prediction according to an embodiment of thepresent disclosure and a method for photovoltaic power forecast based onnumerical weather prediction according to the related art. From FIG. 2and FIG. 3, it may be known that, compared with the method in therelated art, the method according to the embodiments of the presentdisclosure may obtain higher prediction accuracy in daily and weekly.

The embodiments of the present disclosure provide the method forpredicting the photovoltaic power of the photovoltaic power station byaiming at practical demands in industry and academia and consideringdeficiencies of data-driven methods in the related art. The methodcombines the physical model of the photovoltaic power with thedata-driven functionality. The method determines the key weatherfeatures affecting the photovoltaic power through the physical model ofthe photovoltaic power, further uses the data-driven method to fit therelationship among the key features and the photovoltaic power, andaccurately predicts the photovoltaic power. The present disclosure hassignificant improvement in the prediction accuracy.

To achieve the above embodiments, the present disclosure furtherprovides an apparatus for photovoltaic power forecast based on numericalweather prediction. FIG. 4 is a block diagram illustrated an apparatusfor photovoltaic power forecast based on numerical weather predictionaccording to an embodiment of the present disclosure. As illustrated inFIG. 4, the apparatus 1000 may include a computer storage device 1100, aprocessor 1200 and computer programs 1300 stored in the computer storagedevice 1100 and executable by the processor 1200. The computer storagedevice 1100 is configured to store data and the computer programs 1300.The processor 1200 is configured to execute the computer programs 1300to implement the above method according to any one of the aboveembodiments of the present disclosure.

To achieve the above embodiments, the present disclosure furtherprovides non-transitory computer-readable storage medium having storedtherein computer programs. When the computer programs are executed by aprocessor, the above method according to any one of the aboveembodiments of the present disclosure is implemented.

Reference throughout this specification to “an embodiment,” “someembodiments,” “one embodiment”, “another example,” “an example,” “aspecific example,” or “some examples,” means that a particular feature,structure, material, or characteristic described in connection with theembodiment or example is included in at least one embodiment or exampleof the present disclosure. Thus, the appearances of the above phrases invarious places throughout this specification are not necessarilyreferring to the same embodiment or example of the present disclosure.Furthermore, the particular features, structures, materials, orcharacteristics may be combined in any suitable manner in one or moreembodiments or examples. In addition, those skilled in the art maycombine the different embodiments or examples described in thisspecification and features of different embodiments or examples withoutconflicting with each other.

Terms such as “first” and “second” are used herein for purposes ofdescription and are not intended to indicate or imply relativeimportance or significance or imply the number of technical features.Furthermore, the feature defined by “first” or “second” may indicate orimply including at least one feature. In the description of the presentdisclosure, “a plurality or refers to two or more unless otherwisespecified.

Any process or method described in a flow chart or described herein inother ways may be understood to include one or more modules, segments orportions of” codes of executable instructions for achieving specificlogical functions or steps in the process, and the scope of a preferredembodiment of the present disclosure includes other implementations, inwhich the functions may be executed in other orders instead of the orderillustrated or discussed, including in a basically simultaneous manneror in a reverse order, which should be understood by those skilled inthe art.

Any process or method described in a flow chart or described herein inother ways may be understood to be a sequence table of executableinstructions for achieving logical functions, which may be realized inany computer-readable medium for being used by the instruction executionsystem, device or apparatus (for example, the system based on thecomputer, the system including the processor or other systems capable ofreading instructions from the instruction execution system, device orapparatus and executing the instructions) or being used in combinationwith the instruction execution system, device or apparatus. In thespecification, “computer-readable medium” may be any device including,storing, communicating, broadcasting or transmitting programs for beingused by the instruction execution system, device or apparatus or beingused in combination with the instruction execution system, device orapparatus. Specific examples of the computer-readable medium(non-exhaustiveness list) include: electrical connection (electronicdevice) having one or one wires, portable computer disk box (magneticdevice), random access memory (RAM), read only memory (ROM),electrically programmable read-only-memory (EPROM or flash memory),fiber device, and portable CD-ROM. In addition, the computer-readablemedium may even to paper on which programs can be printed or otherappropriate medium, this is because optical scanning may be performed onthe paper or the other medium, and then edit, interpretation or anyother appropriate way if necessary are performed to electrically obtainthe programs, and then the programs are stored in the computer storage.

It should be understood that each part of the present disclosure may berealized by the hardware, software, firmware or their combination. Inthe above embodiments, a plurality of steps or methods may be realizedby the software or firmware stored in the memory and executed by theappropriate instruction execution system. For example, if it is realizedby the hardware, likewise in another embodiment, the steps or methodsmay be realized by one or a combination of the following techniquesknown in the art: a discrete logic circuit having a logic gate circuitfor realizing a logic function of a data signal, an application-specificintegrated circuit having an appropriate combination logic gate circuit,a programmable gate array (PGA), a field programmable gate array (FPGA),etc.

Those skilled in the art shall understand that all or parts of the stepsin the above exemplifying method of the present disclosure may beachieved by commanding the related hardware with programs. The programsmay be stored in a computer readable storage medium, and the programsinclude one or a combination of the steps in the method embodiments ofthe present disclosure when run on a computer.

In addition, each function cell of the embodiments of the presentdisclosure may be integrated in a processing module, or these cells maybe separate physical existence, or two or more cells are integrated in aprocessing module. The integrated module may be realized in a form ofhardware or in a form of software function modules. When the integratedmodule is realized in a form of software function module and is sold orused as a standalone product, the integrated module may be stored in acomputer readable storage medium.

The storage medium mentioned above may be read-only memories, magneticdisks or CD, etc. Although explanatory embodiments have been shown anddescribed, it would be appreciated by those skilled in the art that theabove embodiments cannot be construed to limit the present disclosure,and changes, alternatives, and modifications can be made in theembodiments without departing from scope of the present disclosure.

What is claimed is:
 1. A method for photovoltaic power forecast based onnumerical weather prediction, comprising: reading from a computerstorage device, a plurality of first effective light intensitiescorresponding to a plurality of historical time periods and a pluralityof first photovoltaic array temperatures corresponding to the pluralityof historical time periods; reading from the computer storage device, aplurality of second effective light intensities corresponding to aplurality of prediction time periods and a plurality of secondphotovoltaic array temperatures corresponding to the plurality ofprediction time periods; determining a historical key weather featurematrix based on the plurality of first effective light intensities andthe plurality of first photovoltaic array temperatures as a matrix of:$X_{C}^{h} = \begin{bmatrix}{E_{e}^{h}\lbrack 1\rbrack} & {{E_{e}^{h}\lbrack 1\rbrack} \cdot {T_{c}^{h}\lbrack 1\rbrack}} \\{E_{e}^{h}\lbrack 2\rbrack} & {{E_{e}^{h}\lbrack 2\rbrack} \cdot {T_{c}^{h}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} & {{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} \cdot {T_{c}^{h}\left\lbrack T^{h} \right\rbrack}}\end{bmatrix}$ where, t denotes a historical time period and t ∈{1,2 . .. T^(h)} T^(h) denotes the number of the plurality of historical timeperiods; E_(e) ^(h) [t] denotes the first effective light intensity ofthe historical time period t; T_(c) ^(h) [t] denotes the firstphotovoltaic array temperature of the historical time period t; andX_(C) ^(h) denotes the historical key weather feature matrix;determining a prediction key weather feature matrix based on theplurality of second effective light intensities and the plurality ofsecond photovoltaic array temperatures as a matrix of:$X_{C}^{f} = \begin{bmatrix}{E_{e}^{f}\lbrack 1\rbrack} & {{E_{e}^{f}\lbrack 1\rbrack} \cdot {T_{c}^{f}\lbrack 1\rbrack}} \\{E_{e}^{f}\lbrack 2\rbrack} & {{E_{e}^{f}\lbrack 2\rbrack} \cdot {T_{c}^{f}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} & {{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} \cdot {T_{c}^{f}\left\lbrack T^{f} \right\rbrack}}\end{bmatrix}$ where, t denotes a prediction time period and t ∈{1, 2 .. . T^(f)}; T^(f) denotes the number of the plurality of prediction timeperiods; E_(e) ^(f) [t] denotes the second effective light intensity ofthe prediction time period t; T_(c) ^(f) [t] denotes the secondphotovoltaic array temperature of the prediction time period t; andX_(C) ^(f) denotes the prediction key weather feature matrix; readingfrom the computer storage device, a plurality of first groups of weatherdata corresponding to the plurality of historical time periods;determining a historical weather data matrix based on the plurality offirst groups of weather data; reading from the computer storage device,a plurality of second groups of weather data corresponding to theplurality of prediction time periods; determining a prediction weatherdata matrix based on the plurality of second groups of weather data;determining a historical input matrix based on the historical keyweather feature matrix and the historical weather data matrix;determining a prediction input matrix based on the prediction keyweather feature matrix and the prediction weather data matrix; combiningthe historical input matrix and the prediction input matrix to determinean input matrix; performing singular value decomposition on the inputmatrix to obtain a principal component feature matrix; for a principalcomponent feature corresponding to each prediction time period in theprincipal component feature matrix, calculating a Manhattan distancebetween the principal component feature corresponding to each predictiontime period and the same principal component feature corresponding toeach one of all historical time periods; determining K principalcomponent features corresponding to K historical time periods having thenearest Manhattan distances with the principal component featurecorresponding to each prediction time period; obtaining K photovoltaicpowers corresponding to the K historical time periods from a historicaldatabase; acquiring a fitting relationship according to the K principalcomponent features corresponding to the K historical time periods and Kphotovoltaic powers corresponding to the K historical time periods, thefitting relationship is acquired as g_(t): R^(1×L)→^(1×1), where, g_(t)represents a mapping function; inputting the principal component featurecorresponding to each prediction time period to the fitting relationshipto obtain a photovoltaic power corresponding to each prediction timeperiod.
 2. The method according to claim 1, further comprising:calculating the effective light intensity and recording the effectivelight intensity into the computer storage device.
 3. The methodaccording to claim 2, wherein calculating the effective light intensitycomprises: acquiring a direct sunlight component E_(b), a groundreflection component E_(g) and a sky diffuse component E_(d) by a lightintensity meter, or through a weather database; acquiring a photovoltaicarray light intensity E_(POA) based on the direct sunlight componentE_(b), the ground reflection component E_(g) and the sky diffusecomponent E_(d) by a formula of:E _(POA) =E _(b) +E _(g) +E _(d); measuring a photovoltaic arraycleanliness SF∈[0,1]; acquiring the effective light intensity E_(e)based on the photovoltaic array cleanliness SF and the photovoltaicarray light intensity E_(POA) by a formula of:E _(e) =E _(POA) ·SF.
 4. The method according to claim 1, furthercomprising: calculating the photovoltaic array temperature and recordingthe photovoltaic array temperature into the computer storage device. 5.The method according to claim 4, wherein calculating the photovoltaicarray temperature comprises: acquiring a direct sunlight componentE_(b), a ground reflection component E_(g) and a sky diffuse componentE_(d) by a light intensity meter, or through a weather database;acquiring a photovoltaic array light intensity E_(POA) based on thedirect sunlight component E_(b), the ground reflection component E_(g)and the sky diffuse component E_(d) by a formula of:E _(POA) =E _(b) +E _(g) +E _(d). measuring a wind speed WS and anambient temperature T_(a); acquiring the photovoltaic array temperatureT_(c) based on the photovoltaic array light intensity E_(POA), the windspeed WS and the ambient temperature T_(a) by a formula of:$T_{c} = {T_{a} + \frac{E_{POA}}{U_{0} + {U_{1} \cdot {WS}}} + {\frac{E_{POA}}{E_{0}}\Delta\; T_{,}}}$where, ΔT denotes a temperature difference coefficient of thephotovoltaic array, ΔT>0; U₀ denotes a thermal conductivity constant ofthe photovoltaic array, and U₁ denotes a thermal convection constant ofthe photovoltaic array.
 6. The method according to claim 1, wherein theweather data comprises one or more of an atmospheric pressure, an airsediment, an air humidity, a sky cloud volume, a wind speed, an airtemperature and a surface heat radiation.
 7. The method according toclaim 1, wherein the K principal component features corresponding to theK historical time periods having the nearest Manhattan distances withthe principal component feature corresponding to each prediction timeperiod are determined based on a K-nearest neighbor clustering method.8. An apparatus for photovoltaic power forecast based on numericalweather prediction, comprising: a computer storage device, a processorand computer programs stored in the computer storage device andexecutable by the processor, wherein the computer storage device isconfigured to store a plurality of first effective light intensitiescorresponding to a plurality of historical time periods and a pluralityof first photovoltaic array temperatures corresponding to the pluralityof historical time periods, a plurality of second effective lightintensities corresponding to a plurality of prediction time periods anda plurality of second photovoltaic array temperatures corresponding tothe plurality of prediction time periods, a plurality of first groups ofweather data corresponding to the plurality of historical time periods,and a plurality of second groups of weather data corresponding to theplurality of prediction time periods; a processor is configured to byreading the computer programs to: read from the computer storage devicethe plurality of first effective light intensities corresponding to theplurality of historical time periods and the plurality of firstphotovoltaic array temperatures corresponding to the plurality ofhistorical time periods; read from the computer storage device theplurality of second effective light intensities corresponding to theplurality of prediction time periods and the plurality of secondphotovoltaic array temperatures corresponding to the plurality ofprediction time periods; determine a historical key weather featurematrix based on the plurality of first effective light intensities andthe plurality of first photovoltaic array temperatures as a matrix of:$X_{C}^{h} = \begin{bmatrix}{E_{e}^{h}\lbrack 1\rbrack} & {{E_{e}^{h}\lbrack 1\rbrack} \cdot {T_{c}^{h}\lbrack 1\rbrack}} \\{E_{e}^{h}\lbrack 2\rbrack} & {{E_{e}^{h}\lbrack 2\rbrack} \cdot {T_{c}^{h}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} & {{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} \cdot {T_{c}^{h}\left\lbrack T^{h} \right\rbrack}}\end{bmatrix}$ where, t denotes a historical time period and t ∈{1,2 . .. T^(h)}; T^(h) denotes the number of the plurality of historical timeperiods; E_(e) ^(h) [t] denotes the first effective light intensity ofthe historical time period t; T_(c) ^(h) [t] denotes the firstphotovoltaic array temperature of the historical time period t; andX_(C) ^(h) denotes the historical key weather feature matrix; determinea prediction key weather feature matrix based on the plurality of secondeffective light intensities and the plurality of second photovoltaicarray temperatures as a matrix of: $X_{C}^{f} = \begin{bmatrix}{E_{e}^{f}\lbrack 1\rbrack} & {{E_{e}^{f}\lbrack 1\rbrack} \cdot {T_{c}^{f}\lbrack 1\rbrack}} \\{E_{e}^{f}\lbrack 2\rbrack} & {{E_{e}^{f}\lbrack 2\rbrack} \cdot {T_{c}^{f}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} & {{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} \cdot {T_{c}^{f}\left\lbrack T^{f} \right\rbrack}}\end{bmatrix}$ where, t denotes a prediction time period and t ∈{1, 2 .. . T^(f)}; T^(f) denotes the number of the plurality of prediction timeperiods; E_(e) ^(f) [t] denotes the second effective light intensity ofthe prediction time period t; T_(c) ^(f) [t] denotes the secondphotovoltaic array temperature of the prediction time period t; andX_(C) ^(f) denotes the prediction key weather feature matrix; read fromthe computer storage device the plurality of first groups of weatherdata corresponding to the plurality of historical time periods;determine a historical weather data matrix based on the plurality offirst groups of weather data; read from the computer storage device theplurality of second groups of weather data corresponding to theplurality of prediction time periods; determine a prediction weatherdata matrix based on the plurality of second groups of weather data;determine a historical input matrix based on the historical key weatherfeature matrix and the historical weather data matrix; determine aprediction input matrix based on the prediction key weather featurematrix and the prediction weather data matrix; combine the historicalinput matrix and the prediction input matrix to determine an inputmatrix; perform singular value decomposition on the input matrix toobtain a principal component feature matrix; for a principal componentfeature corresponding to each prediction time period in the principalcomponent feature matrix, calculate a Manhattan distance between theprincipal component feature corresponding to each prediction time periodand the same principal component feature corresponding to each one ofall historical time periods; determine K principal component featurescorresponding to K historical time periods having the nearest Manhattandistances with the principal component feature corresponding to eachprediction time period; obtain K photovoltaic powers corresponding tothe K historical time periods from a historical database; acquire afitting relationship according to the K principal component featurescorresponding to the K historical time periods and K photovoltaic powerscorresponding to the K historical time periods, the fitting relationshipis acquired as g_(t):R^(1×L)→R^(1×1), where, g_(t) represents a mappingfunction; input the principal component feature corresponding to eachprediction time period to the fitting relationship to obtain aphotovoltaic power corresponding to each prediction time period.
 9. Theapparatus according to claim 8, wherein the processor is furtherconfigured to calculate the effective light intensity and record theeffective light intensity into the computer storage device.
 10. Theapparatus according to claim 9, wherein the processor is configured tocalculate the effective light intensity by acts of: acquiring a directsunlight component E_(h), a ground reflection component E_(g) and a skydiffuse component E_(d) by a light intensity meter, or through a weatherdatabase; acquiring a photovoltaic array light intensity E_(POA) basedon the direct sunlight component E_(h), the ground reflection componentE_(g) and the sky diffuse component E_(d) by a formula of:E _(POA) =E _(b) +E _(g) +E _(d); measuring a photovoltaic arraycleanliness SF∈[0,1]; acquiring the effective light intensity E_(e)based on the photovoltaic array cleanliness SF and the photovoltaicarray light intensity E_(POA) by a formula of:E _(e) =E _(POA) ·SF.
 11. The apparatus according to claim 8, whereinthe processor is further configured to calculate the photovoltaic arraytemperature and record the photovoltaic array temperature into thecomputer storage device.
 12. The apparatus according to claim 11,wherein the processor is configured to calculate the photovoltaic arraytemperature by acts of: acquiring a direct sunlight component E_(h), aground reflection component E_(g) and a sky diffuse component E_(d) by alight intensity meter, or through a weather database; acquiring aphotovoltaic array light intensity E_(POA) based on the direct sunlightcomponent E_(b), the ground reflection component E_(g) and the skydiffuse component E_(d) by a formula of:E _(POA) =E _(b) +E _(g) +E _(d); measuring a wind speed WS and anambient temperature T_(a); acquiring the photovoltaic array temperatureT_(c) based on the photovoltaic array light intensity E_(POA), the windspeed WS and the ambient temperature T_(a) by a formula of:$T_{c} = {T_{a} + \frac{E_{POA}}{U_{0} + {U_{1} \cdot {WS}}} + {\frac{E_{POA}}{E_{0}}\Delta\; T_{,}}}$where, ΔT denotes a temperature difference coefficient of thephotovoltaic array, ΔT>0; U₀ denotes a thermal conductivity constant ofthe photovoltaic array, and U₁ denotes a thermal convection constant ofthe photovoltaic array.
 13. The apparatus according to claim 8, whereinthe weather data comprises one or more of an atmospheric pressure, anair sediment, an air humidity, a sky cloud volume, a wind speed, an airtemperature and a surface heat radiation.
 14. The apparatus according toclaim 8, wherein the K principal component features corresponding to theK historical time periods having the nearest Manhattan distances withthe principal component feature corresponding to each prediction timeperiod are determined based on a K-nearest neighbor clustering method.15. A non-transitory computer-readable storage medium having storedtherein instructions that, when executed by a processor of a device,causes the device to perform acts of: reading from a computer storagedevice, a plurality of first effective light intensities correspondingto a plurality of historical time periods and a plurality of firstphotovoltaic array temperatures corresponding to the plurality ofhistorical time periods; reading from the computer storage device, aplurality of second effective light intensities corresponding to aplurality of prediction time periods and a plurality of secondphotovoltaic array temperatures corresponding to the plurality ofprediction time periods; determining a historical key weather featurematrix based on the plurality of first effective light intensities andthe plurality of first photovoltaic array temperatures as a matrix of:$X_{C}^{h} = \begin{bmatrix}{E_{e}^{h}\lbrack 1\rbrack} & {{E_{e}^{h}\lbrack 1\rbrack} \cdot {T_{c}^{h}\lbrack 1\rbrack}} \\{E_{e}^{h}\lbrack 2\rbrack} & {{E_{e}^{h}\lbrack 2\rbrack} \cdot {T_{c}^{h}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} & {{E_{e}^{h}\left\lbrack T^{h} \right\rbrack} \cdot {T_{c}^{h}\left\lbrack T^{h} \right\rbrack}}\end{bmatrix}$ where, t denotes a historical time period and t∈{1,2 . .. T^(h)}; T^(h) denotes the number of the plurality of historical timeperiods; E_(e) ^(h) [t] denotes the first effective light intensity ofthe historical time period t; T_(c) ^(h) [t] denotes the firstphotovoltaic array temperature of the historical time period t; andX_(C) ^(h) denotes the historical key weather feature matrix;determining a prediction key weather feature matrix based on theplurality of second effective light intensities and the plurality ofsecond photovoltaic array temperatures as a matrix of:$X_{C}^{f} = \begin{bmatrix}{E_{e}^{f}\lbrack 1\rbrack} & {{E_{e}^{f}\lbrack 1\rbrack} \cdot {T_{c}^{f}\lbrack 1\rbrack}} \\{E_{e}^{f}\lbrack 2\rbrack} & {{E_{e}^{f}\lbrack 2\rbrack} \cdot {T_{c}^{f}\lbrack 2\rbrack}} \\\vdots & \vdots \\{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} & {{E_{e}^{f}\left\lbrack T^{f} \right\rbrack} \cdot {T_{c}^{f}\left\lbrack T^{f} \right\rbrack}}\end{bmatrix}$ where, t denotes a prediction time period and t∈{1, 2 . .. T^(f)}; T^(f) denotes the number of the plurality of prediction timeperiods; E_(e) ^(f) [t] denotes the second effective light intensity ofthe prediction time period t; T_(c) ^(f) [t] denotes the secondphotovoltaic array temperature of the prediction time period t; andX_(C) ^(f) denotes the prediction key weather feature matrix; readingfrom the computer storage device, a plurality of first groups of weatherdata corresponding to the plurality of historical time periods;determining a historical weather data matrix based on the plurality offirst groups of weather data; reading from the computer storage device,a plurality of second groups of weather data corresponding to theplurality of prediction time periods; determining a prediction weatherdata matrix based on the plurality of second groups of weather data;determining a historical input matrix based on the historical keyweather feature matrix and the historical weather data matrix;determining a prediction input matrix based on the prediction keyweather feature matrix and the prediction weather data matrix; combiningthe historical input matrix and the prediction input matrix to determinean input matrix; performing singular value decomposition on the inputmatrix to obtain a principal component feature matrix; for a principalcomponent feature corresponding to each prediction time period in theprincipal component feature matrix, calculating a Manhattan distancebetween the principal component feature corresponding to each predictiontime period and the same principal component feature corresponding toeach one of all historical time periods; determining K principalcomponent features corresponding to K historical time periods having thenearest Manhattan distances with the principal component featurecorresponding to each prediction time period; obtaining K photovoltaicpowers corresponding to the K historical time periods from a historicaldatabase; acquiring a fitting relationship according to the K principalcomponent features corresponding to the K historical time periods and Kphotovoltaic powers corresponding to the K historical time periods, thefitting relationship is acquired as g_(t): R^(1×L)→R^(1×1), where, g_(t)represents a mapping function; inputting the principal component featurecorresponding to each prediction time period to the fitting relationshipto obtain a photovoltaic power corresponding to each prediction timeperiod.